Their use for dimensionality reduction was popularized in 2006 by Geoffrey Hinton [1]}.
Similar to Principal Component Analysis, a key feature of autoencoders is their ability to encode the input data into latent state representations that provide insight into the samples of the data.
Strictly speaking, an autoencoder only requires a single encoder and a single decoder layer.
However, there are several advantages of adding depth to the architecture of these networks, \(e.g.\)  fewer computations required to represent some functions, fewer data needed to learn some functions [2]}, better compression compared to their shallow or linear counterparts [1]}.
